为什么控制行业后，不报告 Wald chi2

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收藏 2015-04-13

悬赏 80 个论坛币已解决

如下图，做的是GEE模型，原先没有控制行业，会报告报告 Wald chi2；控制了行业以后，不报告报告 Wald chi2，看了help没看懂，求大神解释

这是没有控制行业
GEE population-averaged model                Number of obs    =    4619
Group variable:                   stkcd    Number of groups =    1761
Link:                            identity    Obs per group: min =       1
Family:                         Gaussian                   avg =    2.6
Correlation:                exchangeable                   max =       4
                                             Wald chi2(16)    = 5786.17
Scale parameter:                .2521101    Prob > chi2       = 0.0000
                              (Std. Err. adjusted for clustering on stkcd)
------------------------------------------------------------------------------
         |             Robust
   excess3 |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   excess3 |
      L1. | .8239758 .0116747 70.58 0.000    .8010939 .8468577
         |
instshare |
      L1. | .010215 .0284146    0.36 0.719 -.0454766 .0659067
         |
      mb |
      L1. | -.00102 .0029982 -0.34 0.734 -.0068964 .0048563
         |
      age |
      L1. | .0051386 .0013602    3.78 0.000    .0024727 .0078046
         |
_1stshare |
      L1. | .0365347 .0360073    1.01 0.310 -.0340383 .1071076
         |
   duality |
      L1. | .040558 .0146249    2.77 0.006    .0118938 .0692222
         |
leverage |
      L1. |  -.0201919 .027276 -0.74 0.459 -.0736518    .033268
         |
  boardshare |
      L1. |  -.0452575 .0381067 -1.19 0.235 -.1199452 .0294301
         |
lnboardsize |
      L1. | .0372811 .031684    1.18 0.239 -.0248184 .0993806
         |
independence |
      L1. | .227633 .1122476    2.03 0.043    .0076317 .4476343
         |
      soe |
      L1. |  -.0380747 .0134536 -2.83 0.005 -.0644433 -.0117062
         |
nrmdegree |
      L1. | .073699 .0406658    1.81 0.070 -.0060045 .1534025
         |
lnoverlap |
      L1. | -.014354 .0087102 -1.65 0.099 -.0314257 .0027177
         |
   year2 |       0  (omitted)
   year3 |       0  (omitted)
   year4 |  -.0132148 .0185102 -0.71 0.475 -.0494941 .0230646
   year5 | .0037908 .024105    0.16 0.875 -.0434541 .0510358
   year6 |  -.0400167 .0189373 -2.11 0.035    -.077133 -.0029003
   _cons | .1356121 .1304987    1.04 0.299 -.1201606 .3913849
------------------------------------------------------------------------------

这是控制了行业之后  就不报告 Wald chi2  说模型可能有问题？

GEE population-averaged model                Number of obs    =    4619
Group variable:                   stkcd    Number of groups =    1761
Link:                            identity    Obs per group: min =       1
Family:                         Gaussian                   avg =    2.6
Correlation:                exchangeable                   max =       4
                                             Wald chi2(31)    =       .
Scale parameter:                .2484513    Prob > chi2       =       .
                              (Std. Err. adjusted for clustering on stkcd)
------------------------------------------------------------------------------
         |             Robust
   excess3 |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
   excess3 |
      L1. | .8090153 .0123802 65.35 0.000    .7847505 .8332801
         |
instshare |
      L1. | .0097842 .0281082    0.35 0.728 -.0453069 .0648754
         |
      mb |
      L1. | .0020135 .0032033    0.63 0.530 -.0042648 .0082917
         |
      age |
      L1. | .0045311 .0014789    3.06 0.002    .0016326 .0074296
         |
_1stshare |
      L1. | .0378289 .0366495    1.03 0.302 -.0340028 .1096606
         |
   duality |
      L1. | .043022 .014705    2.93 0.003    .0142007 .0718433
         |
leverage |
      L1. |  -.0406354 .030434 -1.34 0.182    -.100285 .0190142
         |
  boardshare |
      L1. |  -.0555818 .0380719 -1.46 0.144 -.1302014 .0190378
         |
lnboardsize |
      L1. | .0524242 .0325266    1.61 0.107 -.0113267    .116175
         |
independence |
      L1. | .2333201 .1108073    2.11 0.035    .0161418 .4504984
         |
      soe |
      L1. |  -.0379143 .0134215 -2.82 0.005 -.0642199 -.0116088
         |
nrmdegree |
      L1. | .0550519 .0416707    1.32 0.186 -.0266213    .136725
         |
lnoverlap |
      L1. |  -.0120499 .008827 -1.37 0.172 -.0293506 .0052507
         |
industry2 |  -.0217869 .048021 -0.45 0.650 -.1159063 .0723325
industry3 | .0536564 .0391511    1.37 0.171 -.0230784 .1303912
industry4 | .0204298 .0431506    0.47 0.636 -.0641439 .1050035
industry5 | .0742944 .0439032    1.69 0.091 -.0117542 .1603431
industry6 | .0650961 .043184    1.51 0.132 -.0195429 .1497351
industry7 | .0810041 .0435096    1.86 0.063 -.0042732 .1662814
industry8 | .0945619 .078969    1.20 0.231 -.0602146 .2493384
industry9 | .0508076 .0450341    1.13 0.259 -.0374577 .1390728
  industry10 | .1818389 .0434731    4.18 0.000    .0966331 .2670446
  industry11 | .0356629 .049249    0.72 0.469 -.0608633 .1321891
  industry12 | .0641571 .0801337    0.80 0.423 -.0929021 .2212162
  industry13 | .0079661 .0542472    0.15 0.883 -.0983566 .1142887
  industry14 | .0448863    .04331    1.04 0.300 -.0399997 .1297723
  industry15 | .1083271 .0448095    2.42 0.016    .0205021 .1961521
  industry16 | .0625016 .0681902    0.92 0.359 -.0711487 .1961519
  industry17 | .1443289 .0727082    1.99 0.047    .0018234 .2868343
   year2 |       0  (omitted)
   year3 |       0  (omitted)
   year4 |  -.0261889 .0190297 -1.38 0.169 -.0634864 .0111086
   year5 |  -.0084612 .0244063 -0.35 0.729 -.0562968 .0393743
   year6 |  -.0426429 .0187494 -2.27 0.023    -.079391 -.0058948
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